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A Gaussian approach for continuous time models of the short-term interest rate

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Listed author(s):
  • JUN YU

    ()

  • PETER C. B. PHILLIPS

    ()

Abstract

This paper proposes a Gaussian estimator for nonlinear continuous time models of the short-term interest rate. The approach is based on a stopping time argument that produces a normalizing transformation facilitating the use of a Gaussian likelihood. A Monte Carlo study shows that the finite-sample performance of the proposed procedure offers an improvement over the discrete approximation method proposed by Nowman (1997). An em-pirical application to US and British interest rates is given.

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Article provided by Royal Economic Society in its journal The Econometrics Journal.

Volume (Year): 4 (2001)
Issue (Month): 2 ()
Pages: 1-3

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Handle: RePEc:ect:emjrnl:v:4:y:2001:i:2:p:3
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Keywords: Gaussian Estimation; Continuous Time Models; Stochastic Differential Equation; Nonlinear Diffusion; Short-term Interest Rate; Normalizing Transformation; Maximum Likelihood; Level Effect.;

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Cited by:

  1. repec:eco:journ1:2017-02-66 is not listed on IDEAS
  2. Xiao Huang, 2011. "Quasi‐maximum likelihood estimation of discretely observed diffusions," Econometrics Journal, Royal Economic Society, vol. 14(2), pages 241-256, 07.
  3. Chang, Yoosoon, 2012. "Taking a new contour: A novel approach to panel unit root tests," Journal of Econometrics, Elsevier, vol. 169(1), pages 15-28.
  4. Choi, Yongok & Jacewitz, Stefan & Park, Joon Y., 2016. "A reexamination of stock return predictability," Journal of Econometrics, Elsevier, vol. 192(1), pages 168-189.
  5. Jian Huang & Masahito Kobayashi & Michael McAleer, 2010. "Testing the Box-Cox Parameter for an Integrated Process," Working Papers in Economics 10/77, University of Canterbury, Department of Economics and Finance.
  6. Kevin Fergusson & Eckhard Platen, 2015. "Application of Maximum Likelihood Estimation to Stochastic Short Rate Models," Research Paper Series 361, Quantitative Finance Research Centre, University of Technology, Sydney.
  7. McCrorie, J. Roderick & Chambers, Marcus J., 2006. "Granger causality and the sampling of economic processes," Journal of Econometrics, Elsevier, vol. 132(2), pages 311-336, June.
  8. Wymer Clifford R., 2012. "Continuous-Tme Econometrics of Structural Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(2), pages 1-28, April.
  9. Griffin, J.E. & Steel, M.F.J., 2006. "Inference with non-Gaussian Ornstein-Uhlenbeck processes for stochastic volatility," Journal of Econometrics, Elsevier, vol. 134(2), pages 605-644, October.
  10. Terence D.Agbeyegbe & Elena Goldman, 2005. "Estimation of threshold time series models using efficient jump MCMC," Economics Working Paper Archive at Hunter College 406, Hunter College Department of Economics, revised 2005.

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